Tooth loss and its relationship with protein intake by elderly Brazilians—A structural equation modelling approach

Objective

This study aimed at assessing the relationship between self‐perceived tooth loss and wearing dentures, on the one hand, and the consumption of protein, on the other hand, among the elderly population of Botucatu, SP. Food consumption tends to decrease with ageing, especially protein intake, and one of the causes could be the precariousness of oral health. Several risk factors associated with deficient dietary protein intake have been identified, namely greater physical dependence, reduced caloric intake and food insecurity, but no studies have analysed whether tooth loss and prostheses interfere with protein intake.

Methods

An interview was conducted among 365 elderly individuals, in which we examined oral health‐related quality of life (OHRQoL) as the only latent variable, in a 24‐hour nutritional assessment dietary recall repeated 3 times, conducted in person by a trained nutritionist and also performed an analysis of nutritional needs using the Nutrition Data System Research (NDSR) Program.

Results

The structural equation model, performed using Stata v.14, showed that lack of teeth (standardised coefficient [SC] = 0.21, P < .001), and prosthesis use (SC = ?0.21, P < .001) was associated with OHRQoL. Lack of teeth had a direct effect on the consumption of animal protein (SC = 0.08, P = .02), a strong total effect on animal protein intake (SC = 0.51, P = .04) and a medium effect on total protein intake (SC = 0.20, P = .03), adjusted for confounders (depression and medical problems).

Conclusion

Tooth loss had a strong and significant total effect on animal protein intake and a medium effect on total protein intake among elderly Brazilians.     

The Use of Edge-Betweenness Clustering to Investigate Biological Function in Protein Interaction Networks

Background  

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Background  

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Background  

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Validation of protein models by a neural network approach

Background  

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Background  

Current protein clustering methods rely on either sequence or functional similarities between proteins, thereby limiting inferences to one of these areas.     

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Background  

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Background  

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Background  

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Background  

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High-throughput identification of interacting protein-protein binding sites

Background  

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Ab initio and homology based prediction of protein domains by recursive neural networks

Background  

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Background  

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Background  

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Extension of the COG and arCOG databases by amino acid and nucleotide sequences

Background  

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Background  

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Background  

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Background  

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Super paramagnetic clustering of protein sequences

Background  

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